---
title: "Excessive Usage Of Net App"
excerpt: "Account Access Removal
"
categories:
  - Endpoint
last_modified_at: 2021-05-06
toc: true
toc_label: ""
tags:
  - Account Access Removal
  - Impact
  - Splunk Enterprise
  - Splunk Enterprise Security
  - Splunk Cloud
  - Endpoint
---



[Try in Splunk Security Cloud](https://www.splunk.com/en_us/products/cyber-security.html){: .btn .btn--success}

#### Description

This analytic identifies excessive usage of `net.exe` or `net1.exe` within a bucket of time (1 minute). This behavior was seen in a Monero incident where the adversary attempts to create many users, delete and disable users as part of its malicious behavior.

- **Type**: [Anomaly](https://github.com/splunk/security_content/wiki/Detection-Analytic-Types)
- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: [Endpoint](https://docs.splunk.com/Documentation/CIM/latest/User/Endpoint)- **Datasource**: [Splunk Add-on for Sysmon](https://splunkbase.splunk.com/app/5709)
- **Last Updated**: 2021-05-06
- **Author**: Teoderick Contreras, Splunk
- **ID**: 45e52536-ae42-11eb-b5c6-acde48001122


#### Annotations

<details>
  <summary>ATT&CK</summary>

<div markdown="1">


| ID             | Technique        |  Tactic             |
| -------------- | ---------------- |-------------------- |
| [T1531](https://attack.mitre.org/techniques/T1531/) | Account Access Removal | Impact |

</div>
</details>


<details>
  <summary>Kill Chain Phase</summary>

<div markdown="1">

* Exploitation


</div>
</details>


<details>
  <summary>NIST</summary>

<div markdown="1">



</div>
</details>

<details>
  <summary>CIS20</summary>

<div markdown="1">



</div>
</details>

<details>
  <summary>CVE</summary>

<div markdown="1">


</div>
</details>

#### Search 

```

| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime  from datamodel=Endpoint.Processes where `process_net` by Processes.process_name Processes.parent_process_name Processes.original_file_name Processes.dest Processes.user _time span=1m 
| where count >=10 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `excessive_usage_of_net_app_filter`
```

#### Macros
The SPL above uses the following Macros:
* [process_net](https://github.com/splunk/security_content/blob/develop/macros/process_net.yml)
* [security_content_summariesonly](https://github.com/splunk/security_content/blob/develop/macros/security_content_summariesonly.yml)
* [security_content_ctime](https://github.com/splunk/security_content/blob/develop/macros/security_content_ctime.yml)

> :information_source:
> **excessive_usage_of_net_app_filter** is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.

#### Required field
* _time
* Processes.dest
* Processes.user
* Processes.parent_process_name
* Processes.parent_process
* Processes.original_file_name
* Processes.process_name
* Processes.process
* Processes.process_id
* Processes.parent_process_path
* Processes.process_path
* Processes.parent_process_id


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.

#### Known False Positives
unknown. Filter as needed. Modify the time span as needed.

#### Associated Analytic story
* [XMRig](/stories/xmrig)
* [Ransomware](/stories/ransomware)




#### RBA

| Risk Score  | Impact      | Confidence   | Message      |
| ----------- | ----------- |--------------|--------------|
| 28.0 | 40 | 70 | Excessive usage of net1.exe or net.exe within 1m, with command line $process$ has been detected on $dest$ by $user$ |


> :information_source:
> The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author. 

#### Reference

* [https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/](https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/)



#### Test Dataset
Replay any dataset to Splunk Enterprise by using our [replay.py](https://github.com/splunk/attack_data#using-replaypy) tool or the [UI](https://github.com/splunk/attack_data#using-ui).
Alternatively you can replay a dataset into a [Splunk Attack Range](https://github.com/splunk/attack_range#replay-dumps-into-attack-range-splunk-server)


* [https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log](https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log)



[*source*](https://github.com/splunk/security_content/tree/develop/detections/endpoint/excessive_usage_of_net_app.yml) \| *version*: **2**